Food, drink and personal product companies are continually looking for ways to optimise their products and to beat their competitors. One of the approaches used is preference mapping. Two streams of data are collected on products. Consumers from the target market are recruited to taste, drink or use the product samples and score how much they like each sample on a numerical line scale. Specialist panels are trained to score the sensory properties of the samples across a range of attributes that describe and discriminate between samples. Statistical techniques are then used to link the two measures. The consumers are first clustered into groups with similar preferences; multiple correspondence analysis is then used to look for links between the groups and demographic measures. The preferences within each group can be further explored by modelling their average liking versus the sensory characterisation of the samples. The powerful statistical tools and visualisations in JMP make it ideal for consumer science. We will illustrate the application of cluster analysis, multiple correspondence analysis and partial least squares regression using data collected on 12 European apple varieties to find out who likes green apples and why!